Community Deception: Another area of interest that has started revolving very recently is community deception i.e., hiding a target community or the entire community structure from getting exposed to community detection algorithms.
Nagaraja [1]} proposed a counter detection method for hiding a community by adding edges under a certain budget. The endpoints of edges to be added are chosen using vertex centrality measures (degree centrality, eigenvector centrality, and random initialization). Waniek et al. [2]} proposed DICE, an algorithm that deletes intra-community edges (disconnect internal) and adds inter-community edges (connect external), inspired by the functioning of modularity. The authors also devised a metric to quantify the concealment of a target community in the network. Fionda and Pirrò [3]} referred to the problem of hiding a community as community deception. They devised a greedy optimization algorithm (dubbed SADDEN henceforth) to hide a target community based on safeness gain, a new metric that they proposed to quantify how safe a node is under adversarial attack. SADDEN requires the knowledge of the local community rather than knowing the entire community structure of the network to deceive community detection algorithms. Along with this, the authors proposed a metric, called deception score to quantify the effect of the community deception algorithm on the network. They also showed that their method outperforms modularity-based approaches. Recently, Liu et al. [4]} extended the problem of hiding a target community to hiding the entire community structure. They proposed an algorithm for community structure deception based on information theory using network entropy minimization.
